Can Neural Networks Learn Atomic Stick-Slip Friction?

ABSTRACT: Nanofriction experiments typically produce force traces exhibiting atomic stick-slip oscillations, which researchers have traditionally analyzed with ad hoc algorithms. This study successfully unravels the potential of machine learning (ML) to interpret nanofriction force traces and automatically extract Prandtl-Tomlinson (PT) model parameters. A prototypical neural network (NN) perceptron was trained on synthetic force traces generated by simulations across a wide parameter range. Despite its simplicity, this NN successfully analyzed experimental data, marking the first application of a network trained solely on computational data to experimental nanofriction. Challenges encountered in developing the NN model proved to be instructive and revealing. Poor transferability from synthetic to e

SUBMITTER: Shabani M 

PROVIDER: S-EPMC12291085 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature